Assessing the Properties of the WHOQOL-Pain: Quality of Life of Chronic Low Back Pain Patients During Treatment
Bibliographic record
Abstract
OBJECTIVES: Assessing subjective, patient-reported outcomes such as quality of life (QoL) is essential to health care research. This study aimed to assess the properties of a QoL measure relating to pain and discomfort: the WHOQOL-Pain. METHOD: Chronic low back pain patients (n=133) completed the WHOQOL-Pain, SF-12, and short-form McGill Pain Questionnaire before treatment started and again 2-4 weeks later. Of these, 76 received a lumbar epidural steroid injection, and 57 were waiting to receive treatment. RESULTS: Overall, there was no significant difference in effect of either epidural injections or no treatment on bringing about an improvement to QoL overtime. Moderate effect sizes were found for 5 facets including pain relief and uncertainty. Small effect sizes were found for 7 facets including vulnerability, fear and worry, anger and frustration. Larger effect sizes were found for those reporting the most improvement in pain. The waiting group reported no significant changes to QoL but small changes for uncertainty. Three of the four new facets were sensitive to change and test-retest reliability (stability) was confirmed in three. DISCUSSION: Although this study was not designed to test treatment effectiveness, the WHOQOL-Pain enables patients to report changes to important aspects of QoL during many diverse interventions for relieving pain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".